RISSAD: Rule-based Interactive Semi-Supervised Anomaly Detection

Deng, Jiahao; Brown, Eli T.

RISSAD: Rule-based Interactive Semi-Supervised Anomaly Detection

Files

025-029.pdf (340.85 KB)

Date

2021

Authors

Deng, Jiahao
Brown, Eli T.

Publisher

The Eurographics Association

Abstract

Anomaly detection has gained increasing attention from researchers in recent times. Owing to a lack of reliable ground-truth labels, many current state-of-art techniques focus on unsupervised learning, which lacks a mechanism for user involvement. Further, these techniques do not provide interpretable results in a way that is understandable to the general public. To address this problem, we present RISSAD: an interactive technique that not only helps users to detect anomalies, but automatically characterizes those anomalies with descriptive rules. The technique employs a semi-supervised learning approach based on an algorithm that relies on a partially-labeled dataset. Addressing the need for feedback and interpretability, the tool enables users to label anomalies individually or in groups, using visual tools. We demonstrate the tool's effectiveness using quantitative experiments simulated on existing anomaly-detection datasets, and a usage scenario that illustrates a real-world application.

        @inproceedings{10.2312:evs.20211050
,
booktitle = {EuroVis 2021 - Short Papers
},
editor = {Agus, Marco and Garth, Christoph and Kerren, Andreas
},
title = {{RISSAD: Rule-based Interactive Semi-Supervised Anomaly Detection
}},
author = {Deng, Jiahao and 
Brown, Eli T.
},
year = {2021
},
publisher = {The Eurographics Association
},
ISBN = {978-3-03868-143-4
},
DOI = {10.2312/evs.20211050
}
}